An overview of the fundamentals of data management, analysis, and interpretation in quantitative research

Objectives: To provide an overview of three consecutive stages involved in the processing of quantitative research data (ie, data management, analysis, and interpretation) with the aid of practical examples to foster enhanced understanding. Data Sources: Published scientific articles, research textbooks, and expert advice were used. Conclusion: Typically, a considerable amount of numerical research data is collected that require analysis. On entry into a data set, data must be carefully checked for errors and missing values, and then variables must be defined and coded as part of data management. Quantitative data analysis involves the use of statistics. Descriptive statistics help summarize the variables in a data set to show what is typical for a sample. Measures of central tendency (ie, mean, median, mode), measures of spread (standard deviation), and parameter estimation measures (confidence intervals) may be calculated. Inferential statistics aid in testing hypotheses about whether or not a hypothesized effect, relationship, or difference is likely true. Inferential statistical tests produce a value for probability, the P value. The P value informs about whether an effect, relationship, or difference might exist in reality. Crucially, it must be accompanied by a measure of magnitude (effect size) to help interpret how small or large this effect, relationship, or difference is. Effect sizes provide key information for clinical decision-making in health care. Implications for Nursing Practice: Developing capacity in the management, analysis, and interpretation of quantitative research data can have a multifaceted impact in enhancing nurses’ confidence in understanding, evaluating, and applying quantitative evidence in cancer nursing practice.